身份辅助的无序DOA估计关联用于神经语音源跟踪
Identity-Assisted Association of Unordered DOA Estimates for Neural Speech Source Tracking
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中文总结 AI 辅助
针对语音源跟踪中数据关联模糊问题，提出身份辅助关联方法，将无序DOA估计映射到说话人一致轨迹，利用身份嵌入与自注意力机制，实验证明其有效缓解关联混淆。
中文摘要 AI 辅助
由于间歇性语音、空间距离接近以及复杂的声学条件导致的模糊数据关联，语音源跟踪仍然是一个挑战。为解决这些问题，我们提出了一种身份辅助关联方法，将无序到达方向（DOA）估计映射到说话人一致的源轨迹，以实现可靠的语音源跟踪。具体而言，说话人身份嵌入被直接整合到模型输入中，作为空间特征的补充线索。这通过将长期时不变的声纹身份特征与空间线索的短期连续性相结合，来保持身份一致性。为有效处理这些异构输入并适应其不同特性，我们设计了一个统一的神经跟踪器。在该模型中，时间自注意力模块捕获每个源的时间演变，而源自注意力模块则区分竞争的源轨迹。实验结果证明了所提出的神经跟踪器在缓解语音源跟踪中关联混淆方面的优越性。
英文摘要
Tracking speech sources remains a challenge due to ambiguous data association arising from intermittent speech, close spatial proximity, and complex acoustic conditions. To address these issues, we propose an identity-assisted association that maps unordered direction-of-arrival (DOA) estimates to speaker-consistent source trajectories for reliable speech source tracking. Specifically, speaker identity embeddings are directly integrated into the model input as a complementary cue to spatial features. This enables maintaining identity consistency by combining long-term time-invariant vocal identity characteristics with the short-term continuity of spatial cues. To effectively process these heterogeneous inputs while accommodating their distinct characteristics, we design a unified neural tracker. Within this model, time self-attention modules capture the temporal evolution of each source, while source self-attention modules distinguish between competing source tracks. Experimental results demonstrate the superiority of the proposed neural tracker in mitigating association confusion for speech source tracking.
发表机构
- Westlake University（西湖大学）
- Tianjin University（天津大学）
- Zhejiang University（浙江大学）
- Westlake Institute for Advanced Study（西湖高等研究院）
机构由 AI 辅助整理，请以论文原文为准。
